On the Challenges and Opportunities in Generative AI

Laura Manduchi; Clara Meister; Kushagra Pandey; Robert Bamler; Ryan Cotterell; Sina Däubener; Sophie Fellenz; Asja Fischer; Thomas Gärtner; Matthias Kirchler; Marius Kloft; Yingzhen Li; Christoph Lippert; Gerard de Melo; Eric Nalisnick; Björn Ommer; Rajesh Ranganath; Maja Rudolph; Karen Ullrich; Guy Van den Broeck; Julia E Vogt; Yixin Wang; Florian Wenzel; Frank Wood; Stephan Mandt; Vincent Fortuin

arXiv · 2403.00025

On the Challenges and Opportunities in Generative AI

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Abstract

The field of deep generative modeling has grown rapidly in the last few years. With the availability of massive amounts of training data coupled with advances in scalable unsupervised learning paradigms, recent large-scale generative models show tremendous promise in synthesizing high-resolution images and text, as well as structured data such as videos and molecules. However, we argue that current large-scale generative AI models exhibit several fundamental shortcomings that hinder their widespread adoption across domains. In this work, our objective is to identify these issues and highlight key unresolved challenges in modern generative AI paradigms that should be addressed to further enhance their capabilities, versatility, and reliability. By identifying these challenges, we aim to provide researchers with insights for exploring fruitful research directions, thus fostering the development of more robust and accessible generative AI solutions.

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Laura Manduchi, Clara Meister, Kushagra Pandey, Robert Bamler, Ryan Cotterell, Sina Däubener, Sophie Fellenz, Asja Fischer, Thomas Gärtner, Matthias Kirchler, Marius Kloft, Yingzhen Li, Christoph Lippert, Gerard de Melo, Eric Nalisnick, Björn Ommer, Rajesh Ranganath, Maja Rudolph, Karen Ullrich, Guy Van den Broeck, Julia E Vogt, Yixin Wang, Florian Wenzel, Frank Wood, Stephan Mandt, Vincent Fortuin. 2024-02-28. On the Challenges and Opportunities in Generative AI. https://arxiv.org/abs/2403.00025

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